Machine learning classifies premenstrual dysphoric disorder in terms of grey matter structure


Researchers from Uppsala University and Umeå University, led by Erika Comasco have used data-driven methods, such as machine learning, to analyse grey matter measurements in the brain from women with premenstrual dysphoric disorder (PMDD), and compared them to healthy controls. The approach allows to objectively classify neuropsychiatric disorders.

Data-driven methods, such as machine learning, provide a valuable approach for classification of neuropsychiatric disorders.  Psychiatry is hampered by the lack of neurobiological diagnostic and treatment markers, still relying on patients‘self-reports of symptoms. The limited accessibility of the tissue of interest is circumvented by the employment of in vivo neuroimaging techniques such as magnetic resonance imaging (MRI), which allows to measure brain structure. Structural MRI (sMRI) captures the brain morphology. Combined with voxel-based morphometry (VBM), it estimates regional volumes of grey matter, while surface-based morphometry (SBM) enables the reconstruction of the cortical surface and extraction of brain architecture metrics (i.e. cortical thickness, gyrification index, sulcal depth, and cortical complexity).

Photo of the researcher for the study
Erika Comasco, Associate Professor/Associate
senior lecturer at Department of Women's and
Children's Health

In this project, Erika Comasco´s team evaluated grey matter properties in women with premenstrual dysphoric disorder (PMDD) and healthy women. The disorder affects a woman’s ability to work or go to school, and relationships are often harmed by the emotional lability. Symptoms of PMDD peak during the premenstrual phase of a woman’s menstrual cycle and resume upon menstruation onset. Profound irritability or anger, mood swings, anxiety and depression, that are severe enough to negatively impact usual life activities, are key to the diagnosis.

Recently, we provided the first evidence of brain morphological characteristics (i.e., grey matter surface and volume) being related with PMDD symptomatology. Interestingly, the severity of mental and physical PMDD symptoms related to amygdala grey matter volume, with this key region in emotion processing being smaller the more severe the symptoms. In support to this quantitative relationship, we now demonstrated a qualitative association depicted by smaller volume of the amygdala in women with PMDD compared with healthy controls. Additionally, cortical thickness and folding of corticolimbic regions, all regions expressing sex hormone receptors and of relevance to cognitive-affective functions, were related to PMDD symptomatology.

In collaboration with Prof. Marie Bixo and her team from Umea University, we combined our finely characterized neuroimaging datasets to test whether these brain morphological differences would enable us to distinguish women with PMDD from controls. “Based on grey matter probability maps, the volume of specific brain areas contributed to the most to the classification accuracy” says Assoc. Professor Erika Comasco of the Department of Women's and Children's Health. This data-driven findings are expected to help to move the field toward precision psychiatry. Thus, we plan to assess the accuracy of a neuroimaging-based classifier not only for the automated detection of PMDD but also to predict treatment response, and ultimately contribute to advance the field of women´s mental health.

Newsarticle from SciLifeLab



Last modified: 2023-10-23